Peter Dirk Hortensius

Importance sampling for Ising computers using one-dimensional cellular automata

The authors demonstrate that one-dimensional (1-D) cellular automata (CA) form the basis of efficient VLSI architectures for computations involved in the Monte Carlo simulation of the two-dimensional (2-D) Ising model. It is shown that the time-intensive task of importance sampling the Ising configurations is expedited by the inherent parallelism in this approach. The CA architecture further provides a spatially distributed set of pseudorandom numbers that are required in the local nondeterministic decisions at the various sites in the array. The novel approach taken to random-number generation can also be applied to a variety of other highly nondeterministic algorithms from many fields, such as computational geometry, pattern recognition, and artificial intelligence. >

Percolation and anomalous transport as tools in analyzing parallel processing interconnection networks

Abstract It is quite apparent that much of the future advances in computation will be derived through the exploitation of parallel processing. Although a wide variety of topologies have been studied and proposed for both general-purpose and algorithm specific applications, there is still considerable discussion over which architectures are better and why. In this paper we discuss the application of percolation theory and anomalous transport to the issues of defective computer arrays. Percolation theory is used to discuss the static properties of the defective arrays and anomalous transport theory is used to discuss the dynamics of message passing on the defective array. It is hoped that exploiting these analogies will shed additional light on parallel processing issues.